Loading
Loading
We use strictly necessary cookies to run this site, and analytics cookies to understand how it's used. See our Privacy Policy for details.
Adopt AI without expanding your attack surface.
AI services covers two things: assessing the security of AI systems you already use or are building (prompt injection, data leakage, model access control), and building AI-enabled tools and integrations for your business with the same secure-by-design approach applied everywhere else.
AI adoption is moving faster than most organizations' security review processes, tools get integrated, APIs get connected, and data gets shared with third-party models before anyone has assessed what that actually exposes. We help close that gap.
This covers assessing AI systems already in use or in development (prompt injection resistance, data leakage, model access control, third-party AI vendor risk), and separately, building AI-enabled tools and integrations for your business with security considered from the start rather than retrofitted.
AI systems introduce a new class of data-handling risk, what gets sent to a model, logged, or retained isn't always obvious from the vendor's marketing page. A review before wide rollout is far cheaper than a data exposure discovered after the fact.
Organizations that can demonstrate a secure, reviewed approach to AI adoption move faster with customers and partners who are increasingly asking about exactly this in security questionnaires.
AI adoption is outrunning AI governance.
of 2025 data breaches involved shadow or unsanctioned AI use
6
areas covered
What we test against
There's no live exploited-in-the-wild feed for AI-specific vulnerabilities the way there is for CVEs, the risk here is architectural. This is the real, current OWASP ranking we test and build against.
OWASP Top 10 for LLM Applications
OWASP GenAI, 2026Ranked by how often and how severely each actually causes harm
Prompt Injection
Untrusted text treated as an instruction, still #1 every year this list has existed.
Sensitive Information Disclosure
A model trained or fine-tuned on internal data leaking it back out through its responses.
Excessive Agency
An agent permitted to take real actions, API calls, database writes, with no least-privilege scrutiny.
Supply Chain
A compromised or poisoned third-party model, dataset, or plugin.
Unbounded Consumption
Resource-exhaustion and financial-drain attacks against token-billed inference.
How we actually handle prompt injection
An attempted instruction-override, the guardrail that catches it, the safe response the user actually sees, and the audit trail it leaves behind.
An illustrative walkthrough of how prompt-injection handling actually works, not a real client conversation.
Process
Define which AI systems, integrations, or build goals are in scope.
Map data flows, integrations, and access paths involving AI components.
Assess for real exploitation paths, or build the integration securely.
Determine business risk and practical remediation for every finding.
Deliver a prioritized report or a delivered, documented integration.
Verify fixes, or provide ongoing support as usage evolves.
Both, third-party AI tools and APIs you've integrated, and systems you're building in-house. The risk (and the review) looks similar either way: what data goes in, what comes out, and who can access it.
Yes, this is a common starting point. We can review a specific tool or integration before rollout, or work through your broader AI adoption plan with you.
Yes, prompt injection and jailbreak resistance testing is a core part of assessing any AI system that accepts user or third-party input, since it's one of the most common ways an LLM-integrated application's intended behavior gets subverted.
Yes, reviewing what a third-party AI vendor actually does with input data, logging, and retention before you integrate it is exactly the kind of gap this service is meant to close, often before a data-handling issue becomes a real exposure.
Yes, findings are classified against the OWASP Top 10 for Large Language Model Applications and, where relevant, MITRE ATLAS, the adversarial-threat framework specifically for AI systems, so findings use a vocabulary your team or auditors will recognize.
Our methodology for ai services is grounded in established, publicly available frameworks, not a proprietary checklist.
Share a few details about your environment and we'll help you scope the right engagement.